GOAL¶

Work with your customer and other stakeholders to understand and identify the business problems.¶

Formulate questions that define the business goals that the data science techniques can target.¶

Define objectives¶

1. A central objective of this step is to identify the key business variables that the analysis needs to predict. We refer to these variables as the model targets, and we use the metrics associated with them to determine the success of the project. Two examples of such targets are sales forecasts or the probability of an order being fraudulent.

2. Define the project goals by asking and refining "sharp" questions that are relevant, specific, and unambiguous. Data science is a process that uses names and numbers to answer such questions. You typically use data science or machine learning to answer five types of questions:

• How much or how many? (regression)
• Which category? (classification)
• Which group? (clustering)
• Is this weird? (anomaly detection)
• Which option should be taken? (recommendation)
• Determine which of these questions you're asking and how answering it achieves your business goals.

3. Define the project team by specifying the roles and responsibilities of its members. Develop a high-level milestone plan that you iterate on as you discover more information.

4. Define the success metrics. For example, you might want to achieve a customer churn prediction. You need an accuracy rate of "x" percent by the end of this three-month project. With this data, you can offer customer promotions to reduce churn. The metrics must be SMART:

**S**pecific **M**easurable **A**chievable **R**elevant **T**ime-bound